Abstract
Using statistical analysis to create scientifically sound and data-driven results and conclusions is a sound academic practice that minimizes bias and unwanted bias. Conducting statistical analyses is always a tool for researching social science survey data to explore patterns and trends in specific communities further. Pew Research Center should be considered one of the largest and most widely known sociological agencies, constantly expanding the existing insights of society by publishing the results of comprehensive analyses conducted.
This paper proposes to scrutinize the article by Schaeffer (2021), which describes the patterns of Instagram social media usage among American users. This paper revolves around the methodological aspects of sociological research and answers the questions posed in the assignment.
Participants
The first section of Schaeffer’s (2021) paper addresses patterns of social media Instagram usage. It provides several descriptive statistics, allowing a deeper understanding of time slice and dynamic data. To obtain the data, a sociological survey was conducted from January 25 to February 8, 2021, and the respondents were 1,502 American adults (PRC, 2021). The survey was conducted remotely, and Americans used cell phones or landlines to answer the questions. Most individuals were interviewed using a cell phone (1202), and a minority used a landline (300).
Participants were collected for the sample randomly, as random combinations of digits were used for the phone call. Additional sample characteristics included reaching the age of majority (18 years old) and English or Spanish language proficiency to participate in the survey. In terms of age distribution, the sample was represented by 14.6% (n = 220) of people between the ages of 18 and 29, 27.7% (n = 416) of people between the ages of 30 and 49, 25.4% (n = 382) for ages 50 to 64, and 28.6% (n = 429) for anyone over 65.
Transparency and Adequacy
The sample size of this study was 1502 interviewed respondents. There are different ideas about how large the sample should be to achieve representativeness, minimize systematic error, and bias the truth of the results. Since the data collected are used for statistical analysis, there is always a risk of making Errors I and II Types that qualitatively reduce the reliability of the conclusions formed.
In this context, Lakens (2022) suggests an approach to sample size calculation in which both types of errors are equalized because “we believe that the benefit of balanced Type I and Type II error risks often offsets the costs of violating significance level conventions” (p. 20). This condition is possible with samples numerically larger than 1000 observations. Considering that Schaeffer’s (2021) sample was 1502 people, this size exceeds the minimum reliability threshold and is optimal and adequate for a sociological study.
The transparency of the method used should also be discussed. PRC (2021) provides an excellent and clear description of the algorithm for conducting telephone interviews, how the sample was collected, and what inclusion and exclusion criteria were used. The authors, however, do not describe whether participants gave informed consent to be interviewed and whether they were aware of the aims and objectives of the entire project, which is a methodological omission (Nayak & Narayan, 2019). At the same time, PRC (2021) offers a list of problems that may have been related to the sampling design mechanism, among which are the wording of the questions and the practical difficulties of participation.
As it is not stated otherwise, only descriptive methods have been used for statistical analysis. Schaeffer (2021) offered percentages of participants depending on the question and type of answers, which describes frequency distributions. However, the author does provide group comparisons of values in some sections of her study, such as when she says, “Young adults are the most likely group to say they use Instagram” (Schaeffer, 2021, para. 4). It is not known (because it is not reported) whether a parametric ANOVA test was conducted to determine the significance of differences in mean proportions between groups, so it is not possible to judge whether the differences in Instagram usage patterns are valid and statistically significant (Delacre et al., 2019). In other words, in the methodology context, the article has several limitations and omissions that prevent it from being called fully transparent.
Descriptive Statistics
The second section of the study focuses entirely on age patterns in the use of social media; the author provides information on the proportions of participants by age group who said they used Instagram. Thus, the author turns to descriptive statistics in this section, namely the percentage distribution by age group. The rationale behind this data presentation method is to provide a look at age patterns and, therefore, create insights into how social media usage differs between groups. Additionally, this section presents a summary table that presents Instagram usage patterns for different demographic groups, whether broken down by gender, age, race, income, education level, and location, which is the result of previous research on the spectrum of social media, not just Instagram.
Additional Findings
The fifth section of the article focused on a review of children’s Instagram usage statistics despite age restrictions. Specifically, official restrictions prohibit children under the age of 13 from using social media (Harper & Micallef, 2022). However, according to Schaeffer (2021), about 5% of adult respondents stated that their children under 13 years old use social media. This interesting finding shows that Instagram’s age restrictions can be easily circumvented and that parents are not actively preventing it. In other words, in the age of global digitalization, children’s use of social media, although limited, cannot be completely stopped or controlled.
Marketer’s Analysis
The above data has tremendous practical utility for marketing strategies. The age distributions of Instagram usage show which age groups social media is most popular in, which means that for the marketer, it provides valuable information on how to set up targeted advertising and engage with audiences more accurately. The third section describes the frequency of Instagram usage, which, for the marketer, determines how often content can be posted to maintain recognition among the audience. In addition, it was reported that Instagram is not predominantly used as a news platform, which means that a marketer can conclude that it is inappropriate to post news because it is not what subscribers generally expect. In other words, the survey data forms a whole layer of information that marketers can utilize.
References
Delacre, M., Leys, C., Mora, Y. L., & Lakens, D. (2019). Taking parametric assumptions seriously: Arguments for the use of Welch’s F-test instead of the classical F-test in one-way ANOVA. International Review of Social Psychology, 32(1), 1-12. Web.
Harper, P., & Micallef, C. (2022). Child’s play — How old do you have to be to have Facebook and Instagram account? Social media age restrictions explained. The US Sun. Web.
Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 1-32. Web.
Nayak, M. S. D. P., & Narayan, K. A. (2019). Strengths and weaknesses of online surveys. Technology, 6(7), 31-38. Web.
PRC. (2021). Methodology. Pew Research Center. Web.
Schaeffer, K. (2021). 7 facts about Americans and Instagram. Pew Research Center. Web.